Investigate a Dataset - European Soccer Database¶

Data Set¶

European Soccer Database

Overview and Notes¶

This soccer database comes from Kaggle and is well suited for data analysis and machine learning.
It contains data for soccer matches, players, and teams from several European countries from 2008 to 2016.
This dataset is quite extensive, and we encourage you to read more about it here.
The database is stored in a SQLite database. You can access database files using software like DB Browser.
This dataset will help you get good practice with your SQL joins.
Make sure to look at how the different tables relate to each other.
Some column titles should be self-explanatory, and others you’ll have to look up on Kaggle.

Sources¶

http://www.numpy.org

https://pandas.pydata.org

http://pandas.pydata.org/pandas-docs/version/0.9.1/visualization.html

https://matplotlib.org/gallery.html

https://seaborn.pydata.org

https://www.udacity.com/course/intro-to-programming-nanodegree--nd000

https://docs.google.com/document/d/e/2PACX-1vTlVmknRRnfy_4eTrjw5hYGaiQim5ctr9naaRd4V9du2B5bxpd8FEH3KtDgp8qVekw7Cj1GLk1IXdZi/pub?embedded=True

https://www.kaggle.com/hugomathien/soccer

https://github.com/georgezoto/Intro-to-Programming-Nanodegree/blob/fdef27b505a9e64da7b7446846cb7512cf4e9849/Specialization/Data%20Analysis/Investigate%20a%20Dataset%20-%20European%20Soccer%20Database/Investigate%20a%20Dataset%20-%20European%20Soccer%20Database.ipynb

Example Questions¶

How many goals has each team scored home, away and total on each season?
How many goals has each team allowed home, away and total on each season?
How teams improved or deterioted over time and what teams improved or deterioted the most and the least in terms of goals scored or allowed over time?
How many goals has each team allowed home, away and total, from other teams, over all seasons?
Is there such a thing a home advantage?
Which players had the most penalties?
What team attributes lead to the most victories?

Import data analysis modules¶

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sqlite3
import seaborn as sns

Connect to local sqlite database with the following tables:¶

Country, League, Match, Player, Player_Attributes, Team, Team_Attributes¶

connection = sqlite3.connect('database.sqlite')

Run any query, store and display results in a DataFrame¶

Select Country, League, Date, Team Home, Team Away and final score for every match in the dataset that belongs to the Spanish LIGA BBVA league.¶

query = '''
select 
    Country.name as Country, 
    League.name as League, 
    Match.season as Season,
    date(Match.date) as Date,
    Team_home.team_long_name as Team_Home, 
    Team_away.team_long_name as Team_Away, 
    Match.home_team_goal as Team_Home_Goal, 
    Match.away_team_goal as Team_Away_Goal
from
    Country, League, Team as Team_home, Team as Team_away, Match
where
    Country.id = League.country_id AND
    Country.id = Match.country_id AND
    League.id = Match.league_id AND
    Team_home.team_api_id = Match.home_team_api_id AND
    Team_away.team_api_id = Match.away_team_api_id AND
    Country = 'England' AND
    League.name = 'England Premier League'
order by
    Match.date
-- limit 10;
'''

#Run query and store results in a dataframe
spain_liga_bbva_df = pd.read_sql(query,connection)

Print dataframe columns¶

print(spain_liga_bbva_df.columns)

Index([u'Country', u'League', u'Season', u'Date', u'Team_Home', u'Team_Away',
       u'Team_Home_Goal', u'Team_Away_Goal'],
      dtype='object')

Create helpful funtions that will be used repeatedly for resetting an index of an input series, store result in a dataframe, pivot it so that each Season is a seperate column and finally plot its input columns over its input index¶

"""
reset_index_pivot_stacked_bar_plot_series(Args) is used for resetting an index of an input series, store result in a dataframe, pivot it so that each Season is a seperate column and finally plot its input columns over its input index.

Args:
    plot_type: Type of chart plotted, 'stacked_bar' or 'heat_map' 
    season_team_scored_allowed_sum: input pandas Series containing Season, Team and sum of goals scored or allowed for each season 
    index: 'Team_Home' or 'Team_Away' or 'Team' index used for x label plotting
    columns: Columns to pivot resulting dataframe, usually 'Season', used as x label subplotting
    values: 'Team_Home_Goal' or 'Team_Away_Goal' or 'Team_Goal' or 'Team_Allowed' columns used as values of a plot
    title: Title of a plot
    xlabel: X axis label of a plot
    ylabel: Y axis label of a plot
    
Returns:
    Pivoted DataFrame with each Season in a seperate column.

Raises:
    None
"""
def reset_index_pivot_plot_series(plot_type, plot_color, season_team_scored_allowed_sum, \
    input_index, input_columns, input_values, title, xlabel, ylabel, reset_flag, pivot_flag):
    
    if reset_flag:
        #Reset index of series so 'Season' column is fully populated, store in pandas DataFrame
        season_team_scored_allowed_sum = season_team_scored_allowed_sum.reset_index()
    if pivot_flag:
        #Pivot dataframe so that each Season is a seperate column, needed for multi-series plots
        season_team_scored_allowed_sum = season_team_scored_allowed_sum.pivot(index=input_index, columns=input_columns, values=input_values)
    
    if plot_type == 'stacked_bar':
        #Plot a stacked bar of final DataFrame with details provided 
        season_team_scored_allowed_sum.plot(kind='bar', stacked=True, figsize=(20, 15))
    elif plot_type == 'heat_map':
        #Plot a heat map of final DataFrame with details provided 
        plt.subplots(figsize=(20,15))
        sns.heatmap(season_team_scored_allowed_sum, annot=True, cmap=plot_color)
    else:
        #Plot a stacked bar of final DataFrame with details provided 
        season_team_scored_allowed_sum.plot(kind='bar', stacked=True, figsize=(20, 15))
        
    plt.title(title)
    plt.xlabel(xlabel)
    plt.ylabel(ylabel)
    plt.show()
    
    #Return final DataFrame
    return season_team_scored_allowed_sum

Compute home and away goals scored per season and add them up, store in pandas Series¶

season_team_home_goal_sum = spain_liga_bbva_df.groupby(['Season', 'Team_Home'])['Team_Home_Goal'].sum()
season_team_away_goal_sum = spain_liga_bbva_df.groupby(['Season', 'Team_Away'])['Team_Away_Goal'].sum()
season_team_goal_sum = season_team_home_goal_sum + season_team_away_goal_sum
print type(season_team_home_goal_sum)
print season_team_home_goal_sum.head(30)

<class 'pandas.core.series.Series'>
Season     Team_Home           
2008/2009  Arsenal                 31
           Aston Villa             27
           Blackburn Rovers        22
           Bolton Wanderers        21
           Chelsea                 33
           Everton                 31
           Fulham                  28
           Hull City               18
           Liverpool               41
           Manchester City         40
           Manchester United       43
           Middlesbrough           17
           Newcastle United        24
           Portsmouth              26
           Stoke City              22
           Sunderland              21
           Tottenham Hotspur       21
           West Bromwich Albion    26
           West Ham United         23
           Wigan Athletic          17
2009/2010  Arsenal                 48
           Aston Villa             29
           Birmingham City         19
           Blackburn Rovers        28
           Bolton Wanderers        26
           Burnley                 25
           Chelsea                 68
           Everton                 35
           Fulham                  27
           Hull City               22
Name: Team_Home_Goal, dtype: int64

Question 1a: How many goals has each team scored home on each season?¶

Answer 1a: Home goals scored per Season¶

season_team_home_goal_sum = reset_index_pivot_plot_series('stacked_bar', '', season_team_home_goal_sum, \
    'Team_Home', 'Season', 'Team_Home_Goal', 'Home goals scored per Season', 'Team', 'Home Goals Scored', True, True)
season_team_home_goal_sum.head()

season_team_home_goal_sum = reset_index_pivot_plot_series('heat_map', 'YlGnBu', season_team_home_goal_sum, \
    'Team_Home', 'Season', 'Team_Home_Goal', 'Home goals scored per Season', 'Season', 'Team', False, False)

Question 1b: How teams improved over time and what teams improved the most and the least in terms of goals scored home over time?¶

Answer 1b: Home goals scored over Seasons¶

season_team_home_goal_sum_diff = season_team_home_goal_sum.diff(periods=1, axis='columns')

season_team_home_goal_sum_diff = reset_index_pivot_plot_series('heat_map', 'cool', season_team_home_goal_sum_diff, \
    'Team_Home', 'Season', 'Team_Home_Goal', 'Home goals scored differentials over Seasons', 'Season', 'Team', False, False)

Question 2a: How many goals has each team scored away on each season?¶

Answer 2a: Away goals scored per Season¶

season_team_away_goal_sum = reset_index_pivot_plot_series('stacked_bar', '', season_team_away_goal_sum, \
    'Team_Away', 'Season', 'Team_Away_Goal', 'Away goals scored per Season', 'Team', 'Away Goals Scored', True, True)
season_team_away_goal_sum.head()

season_team_away_goal_sum = reset_index_pivot_plot_series('heat_map', 'YlGnBu', season_team_away_goal_sum, \
    'Team_Away', 'Season', 'Team_Away_Goal', 'Away goals scored per Season', 'Season', 'Team', False, False)

Question 2b: How teams improved over time and what teams improved the most and the least in terms of goals scored away over time?¶

Answer 2b: Away goals scored over Seasons¶

season_team_away_goal_sum_diff = season_team_away_goal_sum.diff(periods=1, axis='columns')

season_team_away_goal_sum_diff = reset_index_pivot_plot_series('heat_map', 'cool', season_team_away_goal_sum_diff, \
    'Team_Away', 'Season', 'Team_Away_Goal', 'Away goals scored differentials over Seasons', 'Season', 'Team', False, False)

Question 3a: How many goals has each team scored on each season?¶

Answer 3a: Total goals scored per Season¶

season_team_goal_sum = season_team_goal_sum.reset_index()
season_team_goal_sum.rename(columns={'Season': 'Season', 'Team_Home': 'Team', 0: 'Team_Goal'}, inplace=True)

season_team_goal_sum = reset_index_pivot_plot_series('stacked_bar', '', season_team_goal_sum, \
    'Team', 'Season', 'Team_Goal', 'Total goals scored per Season', 'Team', 'Total Goals Scored', False, True)
season_team_goal_sum.head()

season_team_goal_sum = reset_index_pivot_plot_series('heat_map', 'YlGnBu', season_team_goal_sum, \
    'Team', 'Season', 'Team_Goal', 'Total goals scored per Season', 'Season', 'Team', False, False)

Question 3b: How teams improved over time and what teams improved the most and the least in terms of total goals scored over time?¶

Answer 3b: Total goals scored over Seasons¶

season_team_goal_sum_diff = season_team_goal_sum.diff(periods=1, axis='columns')

season_team_goal_sum_diff = reset_index_pivot_plot_series('heat_map', 'cool', season_team_goal_sum_diff, \
    'Team', 'Season', 'Team_Goal', 'Total goals scored differentials over Seasons', 'Season', 'Team', False, False)

Compute home and away goals allowed per season and add them up, store in pandas Series¶

season_team_home_allowed_sum = spain_liga_bbva_df.groupby(['Season', 'Team_Home'])['Team_Away_Goal'].sum()
season_team_away_allowed_sum = spain_liga_bbva_df.groupby(['Season', 'Team_Away'])['Team_Home_Goal'].sum()
season_team_allowed_sum = season_team_home_allowed_sum + season_team_away_allowed_sum
print type(season_team_home_allowed_sum)
print season_team_home_allowed_sum.head(30)

<class 'pandas.core.series.Series'>
Season     Team_Home           
2008/2009  Arsenal                 16
           Aston Villa             21
           Blackburn Rovers        23
           Bolton Wanderers        21
           Chelsea                 12
           Everton                 20
           Fulham                  16
           Hull City               36
           Liverpool               13
           Manchester City         18
           Manchester United       13
           Middlesbrough           20
           Newcastle United        29
           Portsmouth              29
           Stoke City              15
           Sunderland              25
           Tottenham Hotspur       10
           West Bromwich Albion    33
           West Ham United         22
           Wigan Athletic          18
2009/2010  Arsenal                 15
           Aston Villa             16
           Birmingham City         13
           Blackburn Rovers        18
           Bolton Wanderers        31
           Burnley                 30
           Chelsea                 14
           Everton                 21
           Fulham                  15
           Hull City               29
Name: Team_Away_Goal, dtype: int64

Question 4a: How many goals has each team allowed home on each season?¶

Answer 4a: Home goals allowed per Season¶

season_team_home_allowed_sum = reset_index_pivot_plot_series('stacked_bar', '', season_team_home_allowed_sum, \
    'Team_Home', 'Season', 'Team_Away_Goal', 'Home goals allowed per Season', 'Team', 'Home Goals Allowed', True, True)
season_team_home_allowed_sum.head()

season_team_home_allowed_sum = reset_index_pivot_plot_series('heat_map', 'RdPu', season_team_home_allowed_sum, \
    'Team_Home', 'Season', 'Team_Away_Goal', 'Home goals allowed per Season', 'Season', 'Team', False, False)

Question 4b: How teams deteriorated over time and what teams deteriorated the most and the least in terms of goals allowed home over time?¶

Answer 4b: Home goals allowed over Seasons¶

season_team_home_allowed_sum_diff = season_team_home_allowed_sum.diff(periods=1, axis='columns')

season_team_home_allowed_sum_diff = reset_index_pivot_plot_series('heat_map', 'magma_r', season_team_home_allowed_sum_diff, \
    'Team_Home', 'Season', 'Team_Away_Goal', 'Home goals allowed differentials over Seasons', 'Season', 'Team', False, False)

Question 5a: How many goals has each team allowed away on each season?¶

Answer 5a: Away goals allowed per Season¶

season_team_away_allowed_sum = reset_index_pivot_plot_series('stacked_bar', '', season_team_away_allowed_sum, \
    'Team_Away', 'Season', 'Team_Home_Goal', 'Away goals allowed per Season', 'Team', 'Away Goals Allowed', True, True)
season_team_away_allowed_sum.head()

season_team_away_allowed_sum = reset_index_pivot_plot_series('heat_map', 'RdPu', season_team_away_allowed_sum, \
    'Team_Away', 'Season', 'Team_Home_Goal', 'Away goals allowed per Season', 'Season', 'Team', False, False)

Question 5b: How teams deteriorated over time and what teams deteriorated the most and the least in terms of goals allowed away over time?¶

Answer 5b: Away goals allowed over Seasons¶

season_team_away_allowed_sum_diff = season_team_away_allowed_sum.diff(periods=1, axis='columns')

season_team_away_allowed_sum_diff = reset_index_pivot_plot_series('heat_map', 'magma_r', season_team_away_allowed_sum_diff, \
    'Team_Away', 'Season', 'Team_Home_Goal', 'Away goals allowed differentials over Seasons', 'Season', 'Team', False, False)

Question 6a: How many goals has each team allowed on each season?¶

Answer 6a: Total goals allowed per Season¶

season_team_allowed_sum = season_team_allowed_sum.reset_index()
season_team_allowed_sum.rename(columns={'Season': 'Season', 'Team_Home': 'Team', 0: 'Team_Allowed'}, inplace=True)

season_team_allowed_sum = reset_index_pivot_plot_series('stacked_bar', '', season_team_allowed_sum, \
    'Team', 'Season', 'Team_Allowed', 'Total goals allowed per Season', 'Team', 'Total Goals Allowed', False, True)
season_team_allowed_sum.head()

season_team_allowed_sum = reset_index_pivot_plot_series('heat_map', 'RdPu', season_team_allowed_sum, \
    'Team', 'Season', 'Team_Allowed', 'Total goals allowed per Season', 'Season', 'Team', False, False)

Question 6b: How teams deteriorated over time and what teams deteriorated the most and the least in terms of goals allowed over time?¶

Answer 6b: Total goals allowed over Seasons¶

season_team_allowed_sum_diff = season_team_allowed_sum.diff(periods=1, axis='columns')

season_team_allowed_sum = reset_index_pivot_plot_series('heat_map', 'magma_r', season_team_allowed_sum, \
    'Team', 'Season', 'Team_Allowed', 'Total goals allowed differentials over Seasons', 'Season', 'Team', False, False)

Team matches statistics and forecasts/projections¶

Compute number of unique seasons¶

unique_seasons = pd.Series(spain_liga_bbva_df['Season'].unique())

A reminder of the structure of our original League DataFrame¶

spain_liga_bbva_df.head()

Group original spain_liga_bbva_df DataFrame by Team_Home and Team_Away goals scored and allowed¶

team_match_goal_sum = spain_liga_bbva_df.groupby(['Team_Home', 'Team_Away'])['Team_Home_Goal', 'Team_Away_Goal'].sum()
team_match_goal_sum.head()

Question 7a: How many goals has each team scored home, against other teams, over all seasons?¶

Answer 7a: Home goals scored against other teams¶

team_match_home_goal_sum = reset_index_pivot_plot_series('heat_map', 'YlGnBu', team_match_goal_sum, \
    'Team_Home', 'Team_Away', 'Team_Home_Goal', \
    'Home goals scored over '+str(unique_seasons.size)+' Seasons: '+unique_seasons.iloc[0]+' to '+unique_seasons.iloc[-1], \
    'Team Away', 'Team Home', True, True)

team_match_home_goal_sum.head()

Question 7b: How many goals has each team scored away, against other teams, over all seasons?¶

Answer 7b: Away goals scored against other teams¶

team_match_away_goal_sum = reset_index_pivot_plot_series('heat_map', 'YlGnBu', team_match_goal_sum, \
    'Team_Away', 'Team_Home', 'Team_Away_Goal', \
    'Away goals scored over '+str(unique_seasons.size)+' Seasons: '+unique_seasons.iloc[0]+' to '+unique_seasons.iloc[-1], \
    'Team Home', 'Team Away', True, True)

team_match_away_goal_sum.head()

Question 7c: How many goals has each team scored, against other teams, over all seasons¶

Answer 7c: Total goals scored against other teams¶

team_match_total_goal_sum = team_match_home_goal_sum + team_match_away_goal_sum

team_match_total_goal_sum = reset_index_pivot_plot_series('heat_map', 'YlGnBu', team_match_total_goal_sum, \
    '', '', '', \
    'Total goals scored over '+str(unique_seasons.size)+' Seasons: '+unique_seasons.iloc[0]+' to '+unique_seasons.iloc[-1], \
    'Team', 'Team', False, False)

team_match_total_goal_sum.head()

Question 8a: How many goals has each team allowed home, from other teams, over all seasons?¶

Answer 8a: Home goals allowed from other teams¶

team_match_home_allowed_sum = reset_index_pivot_plot_series('heat_map', 'RdPu', team_match_goal_sum, \
    'Team_Home', 'Team_Away', 'Team_Away_Goal', \
    'Home goals allowed over '+str(unique_seasons.size)+' Seasons: '+unique_seasons.iloc[0]+' to '+unique_seasons.iloc[-1], \
    'Team Away', 'Team Home', True, True)

team_match_home_allowed_sum.head()

Question 8b: How many goals has each team allowed away, from other teams, over all seasons?¶

Answer 8b: Away goals allowed from other teams¶

team_match_away_allowed_sum = reset_index_pivot_plot_series('heat_map', 'RdPu', team_match_goal_sum, \
    'Team_Away', 'Team_Home', 'Team_Home_Goal', \
    'Away goals allowed over '+str(unique_seasons.size)+' Seasons: '+unique_seasons.iloc[0]+' to '+unique_seasons.iloc[-1], \
    'Team Home', 'Team Away', True, True)

team_match_away_allowed_sum.head()

Question 8c: How many goals has each team allowed, from other teams, over all seasons¶

Answer 8c: Total goals allowed from other teams¶

team_match_total_allowed_sum = team_match_home_allowed_sum + team_match_away_allowed_sum

team_match_total_allowed_sum = reset_index_pivot_plot_series('heat_map', 'RdPu', team_match_total_allowed_sum, \
    '', '', '', \
    'Total goals allowed over '+str(unique_seasons.size)+' Seasons: '+unique_seasons.iloc[0]+' to '+unique_seasons.iloc[-1], \
    'Team', 'Team', False, False)

team_match_total_allowed_sum.head()

Apart from individual goals scored or allowed, let us consider a match outcome in terms of win (more goals scored), tie (no goals or equal goals scored) or loss (rival team scored more goals) for each team.¶

"""
categorize_win_tie_loss(team_match_df) is used for categorizing the outcome of match as a win, tie or loss for the home team.

Args:
    team_match_df: Team match DataFrame with columns 'Team_Home_Goal' and 'Team_Away_Goal' available.
Returns:
    Updated DataFrame with 3 new columns created in case of a match home win, tie or loss.

Raises:
    None
"""
def categorize_win_tie_loss(team_match_df):
    #Reference
    #https://pandas.pydata.org/pandas-docs/stable/indexing.html#setting-with-enlargement
    team_match_df.loc[:, 'Team_Home_Win'] = 0
    team_match_df.loc[:, 'Team_Home_Tie'] = 0
    team_match_df.loc[:, 'Team_Away_Win'] = 0
    
    team_match_df.loc[team_match_df['Team_Home_Goal'] > team_match_df['Team_Away_Goal'], 'Team_Home_Win'] = 1
    team_match_df.loc[team_match_df['Team_Home_Goal'] == team_match_df['Team_Away_Goal'], 'Team_Home_Tie'] = 1
    team_match_df.loc[team_match_df['Team_Home_Goal'] < team_match_df['Team_Away_Goal'], 'Team_Away_Win'] = 1
    return team_match_df

Enhance our original League Dataframe with win, tie or loss outcome for each match.¶

spain_liga_bbva_df = categorize_win_tie_loss(spain_liga_bbva_df)
spain_liga_bbva_df.head()

Group original League DataFrame by Team_Home and Team_Away win, tie or loss after each match¶

team_match_outcome_sum = spain_liga_bbva_df.groupby(['Team_Home', 'Team_Away']) \
    ['Team_Home_Win', 'Team_Home_Tie', 'Team_Away_Win'].sum()
team_match_outcome_sum.head()

Let's set a new index for our team_match_outcome_sum Dataframe as the pair of home and away teams of a match¶

https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.set_index.html ¶

team_match_outcome_sum = team_match_outcome_sum.reset_index()
team_match_outcome_sum = team_match_outcome_sum.set_index(['Team_Home', 'Team_Away'])

Create a function that for each pair of teams (team1, team2) will add home wins for team1 with away wins for team1 for each pair of teams (team2, team1). Similarly for ties.¶

"""
add_home_and_away_wins_ties(team_match_df) is used to keep track of wins earned home or away for each pair of teams.
Similalry for ties, home or away.

Args:
    team_match_df: Team match DataFrame with columns ['Team_Home', 'Team_Away', 'Team_Home_Win', 'Team_Home_Tie', 'Team_Away_Win'] available.
Returns:
    Updated DataFrame with 2 new columns created, one for total wins, one for total ties.

Raises:
    None
"""
def add_home_and_away_wins_ties(team_match_df):
    team_match_df.loc[:, 'Team_Win'] = 0
    team_match_df.loc[:, 'Team_Tie'] = 0

    for pair in team_match_outcome_sum.index:
        team_match_df.loc[(pair[0], pair[1]), :]['Team_Win'] = team_match_df.loc[(pair[0], pair[1]), :]['Team_Home_Win'] + team_match_df.loc[(pair[1], pair[0]), :]['Team_Away_Win']
        team_match_df.loc[(pair[0], pair[1]), :]['Team_Tie'] = team_match_df.loc[(pair[0], pair[1]), :]['Team_Home_Tie'] + team_match_df.loc[(pair[1], pair[0]), :]['Team_Home_Tie']

    return team_match_df

team_match_outcome_sum = add_home_and_away_wins_ties(team_match_outcome_sum)

team_match_outcome_sum.head()

Question 9a: How many times did each team win, against other teams, over all seasons?¶

Answer 9a: Wins against other teams¶

team_match_win_sum = reset_index_pivot_plot_series('heat_map', 'autumn_r', team_match_outcome_sum, \
    'Team_Home', 'Team_Away', 'Team_Win', \
    'Wins over '+str(unique_seasons.size)+' Seasons: '+unique_seasons.iloc[0]+' to '+unique_seasons.iloc[-1], \
    'Team', 'Team', True, True)

team_match_win_sum.head()

Question 9b: How many times did each team tie, against other teams, over all seasons?¶

Answer 9b: Ties against other teams¶

Note that this is the first time we encounter a symmetric martix where entry[team1, team2] is the same as entry[team2, team1]¶

team_match_tie_sum = reset_index_pivot_plot_series('heat_map', 'rainbow', team_match_outcome_sum, \
    'Team_Home', 'Team_Away', 'Team_Tie', \
    'Home ties over '+str(unique_seasons.size)+' Seasons: '+unique_seasons.iloc[0]+' to '+unique_seasons.iloc[-1], \
    'Team', 'Team', True, True)

team_match_tie_sum.head()

Final question:¶

Question 10: Is there such a thing a home advantage?¶

Answer 10: Average goals scored home vs away¶

spain_liga_bbva_stats = spain_liga_bbva_df.describe()
spain_liga_bbva_stats['Home_Advantage'] = spain_liga_bbva_stats['Team_Home_Goal'] / spain_liga_bbva_stats['Team_Away_Goal']

spain_liga_bbva_stats

Looking at the Home_Advantage column above we see that on average, a team scores 1.4 more goals when playing home compared to golas scored away.¶

Try it on your own! Feel free to take a deep dive into another Country and League¶

Simply go to cell 8 above where we defined our query to our local sqlite database and change the Country and League name filters to your own favorites.¶

...
Country = 'Spain' AND
League.name = 'Spain LIGA BBVA'
...

Everything until this point will work regardless of which Country and League you selected.¶

The remaining short part applies only to the Spanish BBVA League. As a reminder, you can adopt it to your own rival teams within your favorite League.¶

El Clásico¶

El Clásico is the name given in football to any match between fierce rivals FC Barcelona and Real Madrid.¶

https://en.wikipedia.org/wiki/El_Clásico ¶

All El Clásico matches in our dataset¶

FCB = 'Manchester City'
RMCF = 'Manchester United'
spain_liga_bbva_df[((spain_liga_bbva_df['Team_Home'] == FCB) & (spain_liga_bbva_df['Team_Away'] == RMCF)) \
                  | ((spain_liga_bbva_df['Team_Home'] == RMCF) & (spain_liga_bbva_df['Team_Away'] == FCB))]

Match outcomes in goals when 'FC Barcelona' played at home (see corresponding graph and questions 7a, 8a above)¶

spain_liga_bbva_df[((spain_liga_bbva_df['Team_Home'] == FCB) & (spain_liga_bbva_df['Team_Away'] == RMCF)) ][['Team_Home_Goal','Team_Away_Goal']].sum()

Team_Home_Goal    8
Team_Away_Goal    7
dtype: int64

Match outcomes in goals when 'Real Madrid CF' played at home (see corresponding graph and questions 7b, 8b above)¶

spain_liga_bbva_df[((spain_liga_bbva_df['Team_Home'] == RMCF) & (spain_liga_bbva_df['Team_Away'] == FCB)) ][['Team_Home_Goal','Team_Away_Goal']].sum()

Team_Home_Goal    14
Team_Away_Goal    17
dtype: int64

Match outcomes in number of wins of 'FC Barcelona' over 'Real Madrid CF' (see corresponding graph and questions 9a above)¶

team_match_win_sum.loc[(FCB, RMCF)]

6.0

Match outcomes in number of wins of 'Real Madrid CF' over 'FC Barcelona' (see corresponding graph and questions 9a above)¶

team_match_win_sum.loc[(RMCF, FCB)]

8.0

Match outcomes in number of ties of the pair 'FC Barcelona', 'Real Madrid CF' (see corresponding graph and questions 9b above)¶

team_match_tie_sum.loc[(FCB, RMCF)] #same as team_match_tie_sum.loc[(RMCF, FCB)] since matrix team_match_tie_sum is symmetric

2.0

Additional Material - Appendix¶

Matplotlib colormap reference¶

https://matplotlib.org/examples/color/colormaps_reference.html

Personal favorites for displaying positive impact: 'YlGnBu', 'cool'¶

Personal favorites for displaying negative impact: 'RdPu', 'magma_r', 'plasma_r'¶

#plot_colors = ['Accent', 'Accent_r', 'Blues', 'Blues_r', 'BrBG', 'BrBG_r', 'BuGn', 'BuGn_r', 'BuPu', 'BuPu_r', 'CMRmap', 'CMRmap_r', 'Dark2', 'Dark2_r', 'GnBu', 'GnBu_r', 'Greens', 'Greens_r', 'Greys', 'Greys_r', 'OrRd', 'OrRd_r', 'Oranges', 'Oranges_r', 'PRGn', 'PRGn_r', 'Paired', 'Paired_r', 'Pastel1', 'Pastel1_r', 'Pastel2', 'Pastel2_r', 'PiYG', 'PiYG_r', 'PuBu', 'PuBuGn', 'PuBuGn_r', 'PuBu_r', 'PuOr', 'PuOr_r', 'PuRd', 'PuRd_r', 'Purples', 'Purples_r', 'RdBu', 'RdBu_r', 'RdGy', 'RdGy_r', 'RdPu', 'RdPu_r', 'RdYlBu', 'RdYlBu_r', 'RdYlGn', 'RdYlGn_r', 'Reds', 'Reds_r', 'Set1', 'Set1_r', 'Set2', 'Set2_r', 'Set3', 'Set3_r', 'Spectral', 'Spectral_r', 'Vega10', 'Vega10_r', 'Vega20', 'Vega20_r', 'Vega20b', 'Vega20b_r', 'Vega20c', 'Vega20c_r', 'Wistia', 'Wistia_r', 'YlGn', 'YlGnBu', 'YlGnBu_r', 'YlGn_r', 'YlOrBr', 'YlOrBr_r', 'YlOrRd', 'YlOrRd_r', 'afmhot', 'afmhot_r', 'autumn', 'autumn_r', 'binary', 'binary_r', 'bone', 'bone_r', 'brg', 'brg_r', 'bwr', 'bwr_r', 'cool', 'cool_r', 'coolwarm', 'coolwarm_r', 'copper', 'copper_r', 'cubehelix', 'cubehelix_r', 'flag', 'flag_r', 'gist_earth', 'gist_earth_r', 'gist_gray', 'gist_gray_r', 'gist_heat', 'gist_heat_r', 'gist_ncar', 'gist_ncar_r', 'gist_rainbow', 'gist_rainbow_r', 'gist_stern', 'gist_stern_r', 'gist_yarg', 'gist_yarg_r', 'gnuplot', 'gnuplot2', 'gnuplot2_r', 'gnuplot_r', 'gray', 'gray_r', 'hot', 'hot_r', 'hsv', 'hsv_r', 'icefire', 'icefire_r', 'inferno', 'inferno_r', 'jet', 'jet_r', 'magma', 'magma_r', 'mako', 'mako_r', 'nipy_spectral', 'nipy_spectral_r', 'ocean', 'ocean_r', 'pink', 'pink_r', 'plasma', 'plasma_r', 'prism', 'prism_r', 'rainbow', 'rainbow_r', 'rocket', 'rocket_r', 'seismic', 'seismic_r', 'spectral', 'spectral_r', 'spring', 'spring_r', 'summer', 'summer_r', 'tab10', 'tab10_r', 'tab20', 'tab20_r', 'tab20b', 'tab20b_r', 'tab20c', 'tab20c_r', 'terrain', 'terrain_r', 'viridis', 'viridis_r', 'vlag', 'vlag_r', 'winter', 'winter_r']
plot_colors = ['YlGnBu', 'cool', 'RdPu', 'magma_r', 'plasma_r']

for plot_color in plot_colors:
    season_team_home_goal_sum = reset_index_pivot_plot_series('heat_map', plot_color, season_team_home_goal_sum, \
    'Team_Home', 'Season', 'Team_Home_Goal', plot_color + ' - Home goals scored per Season', 'Team', 'Home Goals Scored', False, False)

Season	2008/2009	2009/2010	2010/2011	2011/2012	2012/2013	2013/2014	2014/2015	2015/2016
Team_Home
Arsenal	31.0	48.0	33.0	39.0	47.0	36.0	41.0	31.0
Aston Villa	27.0	29.0	26.0	20.0	23.0	22.0	18.0	14.0
Birmingham City	NaN	19.0	19.0	NaN	NaN	NaN	NaN	NaN
Blackburn Rovers	22.0	28.0	22.0	26.0	NaN	NaN	NaN	NaN
Blackpool	NaN	NaN	30.0	NaN	NaN	NaN	NaN	NaN

Season	2008/2009	2009/2010	2010/2011	2011/2012	2012/2013	2013/2014	2014/2015	2015/2016
Team_Away
Arsenal	37.0	35.0	39.0	35.0	25.0	32.0	30.0	34.0
Aston Villa	27.0	23.0	22.0	17.0	24.0	17.0	13.0	13.0
Birmingham City	NaN	19.0	18.0	NaN	NaN	NaN	NaN	NaN
Blackburn Rovers	18.0	13.0	24.0	22.0	NaN	NaN	NaN	NaN
Blackpool	NaN	NaN	25.0	NaN	NaN	NaN	NaN	NaN

Season	2008/2009	2009/2010	2010/2011	2011/2012	2012/2013	2013/2014	2014/2015	2015/2016
Team
Arsenal	68.0	83.0	72.0	74.0	72.0	68.0	71.0	65.0
Aston Villa	54.0	52.0	48.0	37.0	47.0	39.0	31.0	27.0
Birmingham City	NaN	38.0	37.0	NaN	NaN	NaN	NaN	NaN
Blackburn Rovers	40.0	41.0	46.0	48.0	NaN	NaN	NaN	NaN
Blackpool	NaN	NaN	55.0	NaN	NaN	NaN	NaN	NaN

Season	2008/2009	2009/2010	2010/2011	2011/2012	2012/2013	2013/2014	2014/2015	2015/2016
Team_Home
Arsenal	16.0	15.0	15.0	17.0	23.0	11.0	14.0	11.0
Aston Villa	21.0	16.0	19.0	25.0	28.0	29.0	25.0	35.0
Birmingham City	NaN	13.0	22.0	NaN	NaN	NaN	NaN	NaN
Blackburn Rovers	23.0	18.0	16.0	33.0	NaN	NaN	NaN	NaN
Blackpool	NaN	NaN	37.0	NaN	NaN	NaN	NaN	NaN

Season	2008/2009	2009/2010	2010/2011	2011/2012	2012/2013	2013/2014	2014/2015	2015/2016
Team_Away
Arsenal	21.0	26.0	28.0	32.0	14.0	30.0	22.0	25.0
Aston Villa	27.0	23.0	40.0	28.0	41.0	32.0	32.0	41.0
Birmingham City	NaN	34.0	36.0	NaN	NaN	NaN	NaN	NaN
Blackburn Rovers	37.0	37.0	43.0	45.0	NaN	NaN	NaN	NaN
Blackpool	NaN	NaN	41.0	NaN	NaN	NaN	NaN	NaN

	Country	League	Season	Date	Team_Home	Team_Away	Team_Home_Goal	Team_Away_Goal
0	England	England Premier League	2008/2009	2008-08-16	Arsenal	West Bromwich Albion	1	0
1	England	England Premier League	2008/2009	2008-08-16	Sunderland	Liverpool	0	1
2	England	England Premier League	2008/2009	2008-08-16	West Ham United	Wigan Athletic	2	1
3	England	England Premier League	2008/2009	2008-08-16	Everton	Blackburn Rovers	2	3
4	England	England Premier League	2008/2009	2008-08-16	Middlesbrough	Tottenham Hotspur	2	1

Team_Away	Arsenal	Aston Villa	Birmingham City	Blackburn Rovers	Blackpool	Bolton Wanderers	Bournemouth	Burnley	Cardiff City	Chelsea	...	Southampton	Stoke City	Sunderland	Swansea City	Tottenham Hotspur	Watford	West Bromwich Albion	West Ham United	Wigan Athletic	Wolverhampton Wanderers
Team_Home
Arsenal	NaN	10.0	3.0	5.0	2.0	6.0	2.0	3.0	2.0	3.0	...	3.0	9.0	8.0	4.0	5.0	2.0	10.0	10.0	7.0	5.0
Aston Villa	3.0	NaN	2.0	4.0	1.0	4.0	1.0	1.0	1.0	3.0	...	1.0	3.0	7.0	1.0	2.0	0.0	5.0	5.0	4.0	2.0
Birmingham City	0.0	0.0	NaN	2.0	2.0	1.0	NaN	1.0	NaN	1.0	...	NaN	2.0	2.0	NaN	0.0	NaN	0.0	2.0	2.0	2.0
Blackburn Rovers	2.0	3.0	1.0	NaN	1.0	3.0	NaN	2.0	NaN	0.0	...	NaN	1.0	1.0	1.0	1.0	NaN	2.0	0.0	3.0	4.0
Blackpool	0.0	0.0	0.0	0.0	NaN	1.0	NaN	NaN	NaN	0.0	...	NaN	1.0	1.0	NaN	1.0	NaN	1.0	0.0	1.0	1.0

	Team_Home_Goal	Team_Away_Goal	Team_Home_Win	Team_Home_Tie	Team_Away_Win	Home_Advantage
count	3040.000000	3040.000000	3040.000000	3040.000000	3040.000000	1.000000
mean	1.550987	1.159539	0.457237	0.257566	0.285197	1.337589
std	1.311615	1.144629	0.498250	0.437365	0.451583	1.145887
min	0.000000	0.000000	0.000000	0.000000	0.000000	NaN
25%	1.000000	0.000000	0.000000	0.000000	0.000000	inf
50%	1.000000	1.000000	0.000000	0.000000	0.000000	1.000000
75%	2.000000	2.000000	1.000000	1.000000	1.000000	1.000000
max	9.000000	6.000000	1.000000	1.000000	1.000000	1.500000